The Evolution of Parkinson’s Disease Diagnostic Criteria and Biological Classification Systems
The Evolution of Parkinson’s Disease Diagnostic Criteria and Biological Classification Systems
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Original abstract
Background: Parkinson's disease (PD) diagnostic approaches have evolved substantially over the past two centuries in parallel with advances in clinical neurology, neuropathology, neuroimaging, genetics, and biomarker science. Recent developments, particularly α-synuclein(αSyn)-based biomarkers, have challenged traditional symptom-based definitions and enabled biologically driven disease classification. Objective: To review the historical evolution of PD diagnostic criteria and compare their conceptual foundations, diagnostic approaches, strengths, limitations, and future implications. Methods: A narrative review of major PD diagnostic frameworks published between 1817 and 2024 was conducted. Included systems comprised the original clinical description by James Parkinson, the UKPDSBB, Calne, Larsen, and Gelb criteria, EFNS/MDS-ES recommendations, MDS Clinical Diagnostic Criteria, MDS Prodromal Criteria and their update, and the SynNeurGe and NSD-ISS frameworks. Results: The evolution of PD diagnosis occurred across four major eras: descriptive clinical diagnosis, clinicopathological criteria, probabilistic and biomarker-supported diagnosis, and biological classification. Early criteria emphasized motor manifestations, exclusion of alternative diagnoses, and responsiveness to dopaminergic therapy. Later frameworks introduced diagnostic certainty levels, ancillary investigations, and Bayesian risk estimation for prodromal disease. The recently proposed SynNeurGe and NSD-ISS frameworks represent a paradigm shift by incorporating biomarkers of αSyn pathology, neurodegeneration, and genetics as disease-defining elements. Conclusions: PD diagnostic criteria have progressively evolved from phenotype-based recognition toward biologically defined classification and staging. Emerging biomarker-based frameworks may enable earlier diagnosis, improved patient stratification, and future precision medicine approaches.